Identifying Image Type in a Capture System

ABSTRACT

Visual objects can be classified according to image type. In one embodiment, the present invention includes capturing a visual object, and decompressing the visual object to a colorspace representation exposing each pixel. The contribution of each pixel to a plurality of image types can then be determined. Then, the contributions can be combined, and the image type of the visual object can be determined based on the contributions.

FIELD OF THE INVENTION

The present invention relates to computer networks, and in particular,to a capture device.

BACKGROUND

Computer networks and systems have become indispensable tools for modernbusiness. Modern enterprises use such networks for communications andfor storage. The information and data stored on the network of abusiness enterprise is often a highly valuable asset. Modern enterprisesuse numerous tools to keep outsiders, intruders, and unauthorizedpersonnel from accessing valuable information stored on the network.These tools include firewalls, intrusion detection systems, and packetsniffer devices. However, once an intruder has gained access tosensitive content, there is no network device that can prevent theelectronic transmission of the content from the network to outside thenetwork. Similarly, there is no network device that can analyse the dataleaving the network to monitor for policy violations, and make itpossible to track down information leeks. What is needed is acomprehensive system to capture, store, and analyse all datacommunicated using the enterprises network.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements and in which:

FIG. 1 is a block diagram illustrating a computer network connected tothe Internet;

FIG. 2 is a block diagram illustrating one configuration of a capturesystem according to one embodiment of the present invention;

FIG. 3 is a block diagram illustrating the capture system according toone embodiment of the present invention;

FIG. 4 is a block diagram illustrating an object assembly moduleaccording to one embodiment of the present invention;

FIG. 5 is a block diagram illustrating an object store module accordingto one embodiment of the present invention;

FIG. 6 is a block diagram illustrating an example hardware architecturefor a capture system according to one embodiment of the presentinvention;

FIG. 7 is a block diagram illustrating an object classification moduleaccording to one embodiment of the present invention;

FIG. 8 is a block diagram illustrating an image classifier according toone embodiment of the present invention;

FIG. 9 is a block diagram illustrating an pixel mapping table accordingto one embodiment of the present invention; and

FIG. 10 is a flow diagram illustrating image classification according toone embodiment of the present invention.

DETAILED DESCRIPTION

Although the present system will be discussed with reference to variousillustrated examples, these examples should not be read to limit thebroader spirit and scope of the present invention. Some portions of thedetailed description that follows are presented in terms of algorithmsand symbolic representations of operations on data within a computermemory. These algorithmic descriptions and representations are the meansused by those skilled in the computer science arts to most effectivelyconvey the substance of their work to others skilled in the art. Analgorithm is here, and generally, conceived to be a self-consistentsequence of steps leading to a desired result. The steps are thoserequiring physical manipulations of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared and otherwise manipulated.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers or the like. It should be borne in mind,however, that all of these and similar terms are to be associated withthe appropriate physical quantities and are merely convenient labelsapplied to these quantities. Unless specifically stated otherwise, itwill be appreciated that throughout the description of the presentinvention, use of terms such as “processing”, “computing”,“calculating”, “determining”, “displaying” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system's registersand memories into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission or display devices.

As indicated above, one embodiment of the present invention isinstantiated in computer software, that is, computer readableinstructions, which, when executed by one or more computerprocessors/systems, instruct the processors/systems to perform thedesignated actions. Such computer software may be resident in one ormore computer readable media, such as hard drives, CD-ROMs, DVD-ROMs,read-only memory, read-write memory and so on. Such software may bedistributed on one or more of these media, or may be made available fordownload across one or more computer networks (e.g., the Internet).Regardless of the format, the computer programming, rendering andprocessing techniques discussed herein are simply examples of the typesof programming, rendering and processing techniques that may be used toimplement aspects of the present invention. These examples should in noway limit the present invention, which is best understood with referenceto the claims that follow this description.

Networks

FIG. 1 illustrates a simple prior art configuration of a local areanetwork (LAN) 10 connected to the Internet 12. Connected to the LAN 102are various components, such as servers 14, clients 16, and switch 18.There are numerous other known networking components and computingdevices that can be connected to the LAN 10. The LAN 10 can beimplemented using various wireline or wireless technologies, such asEthernet and 802.11b. The LAN 10 may be much more complex than thesimplified diagram in FIG. 1, and may be connected to other LANs aswell.

In FIG. 1, the LAN 10 is connected to the Internet 12 via a router 20.This router 20 can be used to implement a firewall, which are widelyused to give users of the LAN 10 secure access to the Internet 12 aswell as to separate a company's public Web server (can be one of theservers 14) from its internal network, i.e., LAN 10. In one embodiment,any data leaving the LAN 10 towards the Internet 12 must pass throughthe router 12. However, there the router 20 merely forwards packets tothe Internet 12. The router 20 cannot capture, analyze, and searchablystore the content contained in the forwarded packets.

One embodiment of the present invention is now illustrated withreference to FIG. 2. FIG. 2 shows the same simplified configuration ofconnecting the LAN 10 to the Internet 12 via the router 20. However, inFIG. 2, the router 20 is also connected to a capture system 22. In oneembodiment, the router 12 splits the outgoing data stream, and forwardsone copy to the Internet 12 and the other copy to the capture system 22.

There are various other possible configurations. For example, the router12 can also forward a copy of all incoming data to the capture system 22as well. Furthermore, the capture system 22 can be configuredsequentially in front of, or behind the router 20, however this makesthe capture system 22 a critical component in connecting to the Internet12. In systems where a router 12 is not used at all, the capture systemcan be interposed directly between the LAN 10 and the Internet 12. Inone embodiment, the capture system 22 has a user interface accessiblefrom a LAN-attached device, such as a client 16.

In one embodiment, the capture system 22 intercepts all data leaving thenetwork. In other embodiments, the capture system can also intercept alldata being communicated inside the network 10. In one embodiment, thecapture system 22 reconstructs the documents leaving the network 10, andstores them in a searchable fashion. The capture system 22 can then beused to search and sort through all documents that have left the network10. There are many reasons such documents may be of interest, includingnetwork security reasons, intellectual property concerns, corporategovernance regulations, and other corporate policy concerns.

Capture System

One embodiment of the present invention is now described with referenceto FIG. 3. FIG. 3 shows one embodiment of the capture system 22 in moredetail. The capture system 22 includes a network interface module 24 toreceive the data from the network 10 or the router 20. In oneembodiment, the network interface module 24 is implemented using one ormore network interface cards (NIC), e.g., Ethernet cards. In oneembodiment, the router 20 delivers all data leaving the network to thenetwork interface module 24.

The captured raw data is then passed to a packet capture module 26. Inone embodiment, the packet capture module 26 extracts data packets fromthe data stream received from the network interface module 24. In oneembodiment, the packet capture module 26 reconstructs Ethernet packetsfrom multiple sources to multiple destinations for the raw data stream.

In one embodiment, the packets are then provided the object assemblymodule 28. The object assembly module 28 reconstructs the objects beingtransmitted by the packets. For example, when a document is transmitted,e.g. as an email attachment, it is broken down into packets according tovarious data transfer protocols such as Transmission ControlProtocol/Internet Protocol (TCP/IP) and Ethernet. The object assemblymodule 28 can reconstruct the document from the captured packets.

One embodiment of the object assembly module 28 is now described in moredetail with reference to FIG. 4. When packets first enter the objectassembly module, they are first provided to a reassembler 36. In oneembodiment, the reassembler 36 groups—assembles—the packets into uniqueflows. For example, a flow can be defined as packets with identicalSource IP and Destination IP addresses as well as identical TCP Sourceand Destination Ports. That is, the reassembler 36 can organize a packetstream by sender and recipient.

In one embodiment, the reassembler 36 begins a new flow upon theobservation of a starting packet defined by the data transfer protocol.For a TCP/IP embodiment, the starting packet is generally referred to asthe “SYN” packet. The flow can terminate upon observation of a finishingpacket, e.g., a “Reset” or “FIN” packet in TCP/IP. If now finishingpacket is observed by the reassembler 36 within some time constraint, itcan terminate the flow via a timeout mechanism. In an embodiment usingthe TPC protocol, a TCP flow contains an ordered sequence of packetsthat can be assembled into a contiguous data stream by the ressembler36. Thus, in one embodiment, a flow is an ordered data stream of asingle communication between a source and a destination.

The flown assembled by the reassembler 36 can then is provided to aprotocol demultiplexer (demux) 38. In one embodiment, the protocol demux38 sorts assembled flows using the TCP Ports. This can includeperforming a speculative classification of the flow contents based onthe association of well-known port numbers with specified protocols. Forexample, Web Hyper Text Transfer Protocol (HTTP) packets—i.e., Webtraffic—are typically associated with port 80, File Transfer Protocol(FTP) packets with port 20, Kerberos authentication packets with port88, and so on. Thus in one embodiment, the protocol demux 38 separatesall the different protocols in one flow.

In one embodiment, a protocol classifier 40 also sorts the flows inaddition to the protocol demux 38. In one embodiment, the protocolclassifier 40—operating either in parallel or in sequence with theprotocol demux 38—applies signature filters to the flows to attempt toidentify the protocol based solely on the transported data. Furthermore,the protocol demux 38 can make a classification decision based on portnumber, which is subsequently overridden by protocol classifier 40. Forexample, if an individual or program attempted to masquerade an illicitcommunication (such as file sharing) using an apparently benign portsuch as port 80 (commonly used for HTTP Web browsing), the protocolclassifier 40 would use protocol signatures, i.e., the characteristicdata sequences of defined protocols, to verify the speculativeclassification performed by protocol demux 38.

In one embodiment, the object assembly module 28 outputs each floworganized by protocol, which represent the underlying objects. Referringagain to FIG. 3, these objects can then be handed over to the objectclassification module 30 (sometimes also referred to as the “contentclassifier”) for classification based on content. A classified flow maystill contain multiple content objects depending on the protocol used.For example, protocols such as HTTP (Internet Web Surfing) may containover 100 objects of any number of content types in a single flow. Todeconstruct the flow, each object contained in the flow is individuallyextracted, and decoded, if necessary, by the object classificationmodule 30.

The object classification module 30 uses the inherent properties andsignatures of various documents to determine the content type of eachobject. For example, a Word document has a signature that is distinctfrom a PowerPoint document, or an Email document. The objectclassification module 30 can extract out each individual object and sortthem out by such content types. Such classification renders the presentinvention immune from cases where a malicious user has altered a fileextension or other property in an attempt to avoid detection of illicitactivity.

In one embodiment, the object classification module 30 determineswhether each object should be stored or discarded. In one embodiment,this determination is based on a various capture rules. For example, acapture rule can indicate that Web Traffic should be discarded. Anothercapture rule can indicate that all PowerPoint documents should bestored, except for ones originating from the CEO's IP address. Suchcapture rules can be implemented as regular expressions, or by othersimilar means. Several embodiments of the object classification module30 are described in more detail further below.

In one embodiment, the capture rules are authored by users of thecapture system 22. The capture system 22 is made accessible to anynetwork-connected machine through the network interface module 24 anduser interface 34. In one embodiment, the user interface 34 is agraphical user interface providing the user with friendly access to thevarious features of the capture system 22. For example, the userinterface 34 can provide a capture rule authoring tool that allows usersto write and implement any capture rule desired, which are then appliedby the object classification module 30 when determining whether eachobject should be stored. The user interface 34 can also providepre-configured capture rules that the user can select from along with anexplanation of the operation of such standard included capture rules. Inone embodiment, the default capture rule implemented by the objectclassification module 30 captures all objects leaving the network 10.

If the capture of an object is mandated by the capture rules, the objectclassification module 30 can also determine where in the object storemodule 32 the captured object should be stored. With reference to FIG.5, in one embodiment, the objects are stored in a content store 44memory block. Within the content store 44 are files 46 divided up bycontent type. Thus, for example, if the object classification moduledetermines that an object is a Word document that should be stored, itcan store it in the file 46 reserved for Word documents. In oneembodiment, the object store module 32 is integrally included in thecapture system 22. In other embodiments, the object store module can beexternal—entirely or in part—using, for example, some network storagetechnique such as network attached storage (NAS) and storage areanetwork (SAN).

Tag Data Structure

In one embodiment, the content store is a canonical storage location,simply a place to deposit the captured objects. The indexing of theobjects stored in the content store 44 is accomplished using a tagdatabase 42. In one embodiment, the tag database 42 is a database datastructure in which each record is a “tag” that indexes an object in thecontent store 44 and contains relevant information about the storedobject. An example of a tag record in the tag database 42 that indexesan object stored in the content store 44 is set forth in Table 1:

TABLE 1 Field Name Definition MAC Address Ethernet controller MACaddress unique to each capture system Source IP Source Ethernet IPAddress of object Destination IP Destination Ethernet IP Address ofobject Source Port Source TCP/IP Port number of object Destination PortDestination TCP/IP Port number of the object Protocol IP Protocol thatcarried the object Instance Canonical count identifying object within aprotocol capable of carrying multiple data within a single TCP/IPconnection Content Content type of the object Encoding Encoding used bythe protocol carrying object Size Size of object Timestamp Time that theobject was captured Owner User requesting the capture of object (ruleauthor) Configuration Capture rule directing the capture of objectSignature Hash signature of object Tag Signature Hash signature of allpreceding tag fields

There are various other possible tag fields, and some embodiments canomit numerous tag fields listed in Table 1. In other embodiments, thetag database 42 need not be implemented as a database, and a tag neednot be a record. Any data structure capable of indexing an object bystoring relational data over the object can be used as a tag datastructure. Furthermore, the word “tag” is merely descriptive, othernames such as “index” or “relational data store,” would be equallydescriptive, as would any other designation performing similarfunctionality.

The mapping of tags to objects can, in one embodiment, be obtained byusing unique combinations of tag fields to construct an object's name.For example, one such possible combination is an ordered list of theSource IP, Destination IP, Source Port, Destination Port, Instance andTimestamp. Many other such combinations including both shorter andlonger names are possible. In another embodiment, the tag can contain apointer to the storage location where the indexed object is stored.

The tag fields shown in Table 1 can be expressed more generally, toemphasize the underlying information indicated by the tag fields invarious embodiments. Some of these possible generic tag fields are setforth in Table 2:

TABLE 2 Field Name Definition Device Identity Identifier of capturedevice Source Address Origination Address of object DestinationDestination Address of object Address Source Port Origination Port ofobject Destination Port Destination Port of the object Protocol Protocolthat carried the object Instance Canonical count identifying objectwithin a protocol capable of carrying multiple data within a singleconnection Content Content type of the object Encoding Encoding used bythe protocol carrying object Size Size of object Timestamp Time that theobject was captured Owner User requesting the capture of object (ruleauthor) Configuration Capture rule directing the capture of objectSignature Signature of object Tag Signature Signature of all precedingtag fields

For many of the above tag fields in Tables 1 and 2, the definitionadequately describes the relational data contained by each field. Forthe content field, the types of content that the object can be labeledas are numerous. Some example choices for content types (as determined,in one embodiment, by the object classification module 30) are JPEG,GIF, BMP, TIFF, PNG (for objects containing images in these variousformats); Skintone, PDF, MSWord, Excel, PowerPoint, MSOffice (forobjects in these popular application formats); HTML, WebMail, SMTP, FTP(for objects captured in these transmission formats); Telnet, Rlogin,Chat (for communication conducted using these methods); GZIP, ZIP, TAR(for archives or collections of other objects); Basic_Source,C++_Source, C_Source, Java_Source, FORTRAN_Source, Verilog_Source,VHDL_Source, Assembly_Source, Pascal_Source, Cobol_Source, Ada_Source,Lisp_Source, Perl_Source, XQuery_Source, Hypertext Markup Language,Cascaded Style Sheets, JavaScript, DXF, Spice, Gerber, Mathematica,Matlab, AllegroPCB, ViewLogic, TangoPCAD, BSDL, C_Shell, K_Shell,Bash_Shell, Bourne_Shell, FTP, Telnet, MSExchange, POP3, RFC822, CVS,CMS, SQL, RTSP, MIME, PDF, PS (for source, markup, query, descriptive,and design code authored in these high-level programming languages); CShell, K Shell, Bash Shell (for shell program scripts); Plaintext (forotherwise unclassified textual objects); Crypto (for objects that havebeen encrypted or that contain cryptographic elements); Englishtext,Frenchtext, Germantext, Spanishtext, Japanesetext, Chinesetext,Koreantext, Russiantext (any human language text); Binary Unknown, ASCIIUnknown, and Unknown (as catchall categories).

The signature contained in the Signature and Tag Signature fields can beany digest or hash over the object, or some portion thereof. In oneembodiment, a well-known hash, such as MD5 or SHA1 can be used. In oneembodiment, the signature is a digital cryptographic signature. In oneembodiment, a digital cryptographic signature is a hash signature thatis signed with the private key of the capture system 22. Only thecapture system 22 knows its own private key, thus, the integrity of thestored object can be verified by comparing a hash of the stored objectto the signature decrypted with the public key of the capture system 22,the private and public keys being a public key cryptosystem key pair.Thus, if a stored object is modified from when it was originallycaptured, the modification will cause the comparison to fail.

Similarly, the signature over the tag stored in the Tag Signature fieldcan also be a digital cryptographic signature. In such an embodiment,the integrity of the tag can also be verified. In one embodiment,verification of the object using the signature, and the tag using thetag signature is performed whenever an object is presented, e.g.,displayed to a user. In one embodiment, if the object or the tag isfound to have been compromised, an alarm is generated to alert the userthat the object displayed may not be identical to the object originallycaptured.

Image Classification

A user may be interested in the type of image and graphical objectsbeing transmitted over the network. Furthermore, a user may want toquery to system for object containing a specific type of image. Also,some policies regarding object transmission may depend on the type of animage. For example, a user may want to set up a policy to intercept allincoming or outgoing pornographic images. Alternatively, a user may wantto search all emails sent out last week containing an X-ray image. Thus,in one embodiment, the capture system 22 identifies the image type ofvisual, image, or graphical objects. This identification can then beinserted into a tag associated with the object to help process userqueries.

One embodiment of the present invention is now described with referenceto FIG. 7. In the embodiment described with reference to FIG. 7, theimage classification functionality is implemented in the objectclassification module 30 described above. However, the imageclassification process and modules may be implemented in other parts ofthe capture system 22 or as a separate module.

FIG. 7 illustrates a detailed diagram of one embodiment of the objectclassification module 30. Objects arriving from the object assemblymodule 28 are forwarded to the content store, and used to generate thetag to be associated with the object. For example, one module called thecontent classifier 62 can determine the content type of the object. Thecontent type is then forwarded to the tag generator 68 where it isinserted into the content field described above. Various other suchprocessing, such as protocol and size determination, is represented byother processing block 66.

In one embodiment, the image classifier 64 identifies the image type fora visual object that can be inserted into an field of the tag by the taggenerator 68. For example, the tag described with reference to Table 1and Table 2 can have an additional filed called “Image Type,” or “ColorTone,” or some other similar descriptive name. In one embodiment, theimage classifier 64 is only activated if the content classifier 62identifies the object as a visual object.

Image types or color tone types (these two descriptive terms are usedinterchangeably herein) are identifiable because certain kind of visualobjects have identifiable patters of colors, color variations, shades ortextures. For example, most astrological images contain a lot of blackpixels.

A pixel (or picture element) is a basic unit of programmable color on acomputer display or in a computer image. The color of a pixel can bedescribed digitally according to a color space. For example, in the RBGcolor space, a pixel describes some blend of red, blue, and greencomponents of the color spectrum. A 24-bit color system uses one byte tospecify the intensity of each component. There are other color spaces.Pixels and their representations are well understood by those skilled inthe art.

One embodiment of the image classifier 64 is now described withreference to FIG. 8. The visual object, in one embodiment, if firstreceived by a scaling module 80. The scaling module 80 selects a portionof the image, up to the entire image. For example, for a large image, itmay be efficient to base image classification on a portion of the image.For example, whether an image is an astronomical photograph or not canbe gleaned from a relatively small portion of a large image. Similarly,whether an image includes skintone can also be gleaned from a portion ofthe image. A skintone image can be defined as an image containingprimarily pixels with an RGB color space similar to that of human skin.Further distinctions regarding the percentage of the image containingsuch RGB space are also possible, such as skintone divisions byethnicity. Although a skintone image is likely to be pornographic innature, it may also be an image of an inanimate object that happens tobe a color within the RGB color space defined for skintone.

In one embodiment, the scaling module 80 performs scaling based onobject size and shape. For a very large square object, for example, thescaling module 80 may select the middle ninth in the center of theimage. For a long rectangular banner-like object, for example, theentire height would be selected, but only the middle third of the width.For smaller objects or high-priority objects, the entire object may beselected.

The selected portion of the visual object is then passed to thedecompressor 82. The decompressor 82 decompresses the image from itsnative format—such as Joint Photographic Experts Group (JPEG) orGraphics Interchange Format (GIF)—into raw pixels of a standard colorspace, such as the 24-bit RBG color space. Thus, the output of thedecompressor 82 is a group of pixels. Image decompression is wellunderstood by those skilled in the art.

In one embodiment, the pixels from the decompressor 82 are passed to aquantizer 84. The quantizer 84 may quantize the pixels to reduce thenumber of possible pixels. For example, the quantizer 84 may only takethe seven most significant bits of each pixel. Both the scaling module80 and the quantizer 84 are optional and may be omitted from embodimentsof the present invention that do not utilize scaling of the visualobject or quantizing of the pixels.

In one embodiment, the pixels are provided to the pixel map module 86.The pixel map module 86, in one embodiment, maps each pixel to acontribution of the pixel to various image types. For example, in oneembodiment, the image types can be skintone (human skin color), X-ray,graph images, and astrological. In a real-world embodiment, there can bemany more image classifications or types including but not limited togeographic images, circuit design masks, banner advertisements andgreyscales. Any type of image or visual object having distinguishingcolor characteristics can be given its classification using training.

However, in this simplified embodiment with only four possible imagetypes, the pixel map module 86 maps a pixel to the contribution of thepixel to the likelihood that the image contains human skin, an X-rayimage, a graph, or an astrological image. In one embodiment, the pixelmap module 86 performs this mapping by accessing a pixel mapping table,an example of which is now discussed with reference to FIG. 9.

FIG. 9 shows a table associating each pixel in a color space with acontribution to four possible image types. FIG. 9 actually shows twosets of contributions, Contribution A and Contribution B, to bediscussed further below.

For example, using Contribution A of the table shown in FIG. 9, pixel(0,0,4)—a very light blue pixel—would map to (0,1,2,1). This means thatpixel (0,0,4) contributes 0 to the image possible being skintone, 1 tothe image possible being an X-ray, 2 to the image possible being agraph, and 1 to the image possible being an astrological image. (This isjust an example, not a real world mapping) The values 0 to 2 may begiven names such as zero contribution for 0, weak contribution for 1,and strong contribution to 2.

As seen in FIG. 9, in one embodiment, the pixel map module 86 can usedifferent mappings for different pixels or groups of pixels. Forexample, certain colors may have more significance if found in specificlocations in an image. Thus, pixels from the center of an image, forexample, may be mapped using a different map than pixels from the edgeof the image.

As an example, the pixel map table in FIG. 9 has two maps, shown asContribution A and Contribution B. Other embodiments, can implement moremaps as well. Thus, the pixel map module 86 can select which mapping touse based on information about each pixel.

Referring again to FIG. 8, in one embodiment, the contributions of eachpixel to the possible image types—as determined by the pixel map module86—are provided to the classification module 88. In one embodiment, theclassification module calculates the sum of all contributions for allpixels. This can be done in parallel with the pixel map module 86performing the mapping. In this example, the end sum will be in the formof a four-tuple such as (12453, 354553, 25545, 53463), with each numberof the four-tuple representing the contribution of all selected pixelsto each possible image type.

In one embodiment, the classification module 88 next determines theimage type (or multiple possible image types), by comparing thecontributions against a threshold. For example, X-ray may be defined asanything about 200,000 to X-ray contribution (for an image thisparticular size) is probably an X-ray. If multiple thresholds are met,then multiple classifications may be assigned. Alternately, how much thethreshold is exceeded may be a factor to elimination one of the possibleimage types.

In one embodiment, the thresholding performed by the classificationmodule 88 is sensitive to the size of the image, and more specifically,to the number of pixels selected by the scaling module 80. Image typescan be defined in terms of percentages, which may need to be translatedto thresholds by the classification module 88. For example, anastrological image can be defined as an image having 95 percent black or“near black” pixels. The threshold for this definition can than becalculated as 1.9 times the number of pixels sampled, where 2 is thestrong contribution.

Both the definitions of different image types and the pixel map tableshown in FIG. 9 can be populated by training the system using knownimages. For example, by recording the RGB pixel histograms of severalsample images, certain “strong” indicators (RGB values of strongpresence in the samples) could be identified. Additionally, “weak”indicators (RGB values missing or present in only small quantities) canbe obtained.

In one embodiment, by creating a contribution mapping with the strongindicators at 2, the weak indicators at zero, and the remaining RGBcombinations at 1, a first attempt at “training” to recognize a newimage type can be accomplished. This training would include furtheriterations based on negative samples (images deemed not to belong to thenew image class). In one embodiment, for such “negative training” theindicators would be reversed.

The image classification, or classifications, as determined by the imageclassifier 64 can be inserted into the tag generated by the taggenerator 68, or it can become associated with the visual object in someother manner. In one embodiment, the image type is appended to thecontent filed in the case of visual objects. For example, the contentfiled would say “JPEG image::Skintone.”

One simplified embodiment of visual object classification processing isnow described with reference to FIG. 10. In block 102 a visual object iscaptured over a network, the visual object containing some form ofvisual content such as an image, graphics, or other type of visualcontent. In block 104, the visual object is decompressed to expose itspixels.

In block 106, the contribution of each pixel to each possible image typeis calculated. This can be done by using one or more mappings asexplained with reference to FIG. 9. Finally, in block 108, the visualobject is classified by assigning an image type to the object.

General Matters

In several embodiments, the capture system 22 has been described aboveas a stand-alone device. However, the capture system of the presentinvention can be implemented on any appliance capable of capturing andanalyzing data from a network. For example, the capture system 22described above could be implemented on one or more of the servers 14 orclients 16 shown in FIG. 1. The capture system 22 can interface with thenetwork 10 in any number of ways, including wirelessly.

In one embodiment, the capture system 22 is an appliance constructedusing commonly available computing equipment and storage systems capableof supporting the software requirements. In one embodiment, illustratedby FIG. 6, the hardware consists of a capture entity 46, a processingcomplex 48 made up of one or more processors, a memory complex 50 madeup of one or more memory elements such as RAM and ROM, and storagecomplex 52, such as a set of one or more hard drives or other digital oranalog storage means. In another embodiment, the storage complex 52 isexternal to the capture system 22, as explained above. In oneembodiment, the memory complex stored software consisting of anoperating system for the capture system device 22, a capture program,and classification program, a database, a filestore, an analysis engineand a graphical user interface.

Thus, a capture system and a word indexing scheme for the capture systemhave been described. In the forgoing description, various specificvalues were given names, such as “objects,” and various specific modulesand tables, such as the “attribute module” and “general expressiontable” have been described. However, these names are merely to describeand illustrate various aspects of the present invention, and in no waylimit the scope of the present invention. Furthermore various modulescan be implemented as software or hardware modules, or without dividingtheir functionalities into modules at all. The present invention is notlimited to any modular architecture either in software or in hardware,whether described above or not.

1-22. (canceled)
 23. A method to be executed in an electronicenvironment, comprising: receiving an object sought to be transmittedover a network; determining a contribution of pixels associated with theobject; and determining an image type of the object based on thecontribution of the pixels.
 24. The method of claim 23, wherein thedetermining of the contribution of the pixels comprises mapping aselected pixel to possible values to quantify a contribution of theselected pixel to the image type.
 25. The method of claim 24, whereinthe mapping of the selected pixel is based on a position of the selectedpixel within the object.
 26. The method of claim 24, wherein the mappingof the selected pixel comprises mapping the selected pixel to thepossible values that indicate no contribution, a weak contribution, or astrong contribution to a particular image type.
 27. The method of claim24, wherein the mapping is different for particular pixels at an edge ofthe object than for particular pixels in a center area of the object.28. The method of claim 23, further comprising: selecting a portion ofthe object for determining the contribution of the pixels associatedwith the object.
 29. The method of claim 28, wherein the selecting ofthe portion is based on a size, a shape, or particular dimensions of theobject.
 30. The method of claim 23, further comprising: decompressing anobject from its native format to a plurality of pixels of a color space.31. The method of claim 23, further comprising: classifying the objectas related to the image type if a sum of the contributions of the pixelsof the image type exceeds a predetermined threshold.
 32. The method ofclaim 31, wherein exceeding the threshold is used to eliminate one ofthe image types from possible consideration as a correct image type forthe object.
 33. The method of claim 23, wherein the image type comprisesa plurality of image types that include one or more of a skintone imagetype, an x-ray image type, a grayscale image type, a graph image type,an astrological image type, a geographic image type, a circuit designmask image type, and a banner advertisement image type.
 34. The methodof claim 23, further comprising: configuring a policy that outlines oneor more image types that are prohibited from being transmitted over thenetwork.
 35. The method of claim 23, further comprising: querying forone or more image types sought to be transmitted over the network. 36.The method of claim 23, further comprising: providing a tag thatidentifies the object as being related to a particular image type. 37.The method of claim 36, further comprising: storing the tag in adatabase, wherein the tag can be used in processing a query for one ormore image types.
 38. An apparatus, comprising: a capture elementconfigured to receive an object sought to be transmitted over a network;a classification module configured to determine a contribution of pixelsassociated with the object; and an image classifier module configured todetermine an image type of the object based on the contribution of thepixels.
 39. The apparatus of claim 38, further comprising: a pixel mapmodule configured to map a selected pixel to possible values to quantifya contribution of the selected pixel to the image type.
 40. Theapparatus of claim 39, wherein the mapping of the selected pixel isbased on a position of the selected pixel within the object.
 41. Theapparatus of claim 39, wherein the mapping of the selected pixelcomprises mapping the selected pixel to the possible values thatindicate no contribution, a weak contribution, or a strong contributionto a particular image type.
 42. The apparatus of claim 39, wherein themapping is different for particular pixels at an edge of the object thanfor particular pixels in a center area of the object.
 43. The apparatusof claim 38, further comprising: a scaling module configured to select aportion of the object for determining the contribution of the pixelsassociated with the object.
 44. The apparatus of claim 43, wherein theselecting of the portion is based on a size, a shape, or particulardimensions of the object.
 45. The apparatus of claim 38, furthercomprising: a decompressing module configured to decompress an objectfrom its native format to a plurality of pixels of a color space. 46.The apparatus of claim 38, further comprising: a tag generatorconfigured to provide a tag that identifies the object as being relatedto a particular image type.
 47. The apparatus of claim 46, furthercomprising: a memory element configured to store the tag, wherein thetag can be used in processing a query for one or more image types. 48.Logic encoded in one or more tangible media for execution and whenexecuted by a processor is operable to: receive an object sought to betransmitted over a network; determine a contribution of pixelsassociated with the object; and determine an image type of the objectbased on the contribution of the pixels.
 49. The logic of claim 48,wherein the determining of the contribution of the pixels comprisesmapping a selected pixel to possible values to quantify a contributionof the selected pixel to the image type, wherein the mapping of theselected pixel is based on a position of the selected pixel within theobject.
 50. The logic of claim 48, wherein the code is further operableto: select a portion of the object for determining the contribution ofthe pixels associated with the object, wherein the selecting of theportion is based on a size, a shape, or particular dimensions of theobject.
 51. The logic of claim 48, wherein the code is further operableto classify the object as related to the image type if a sum of thecontributions of the pixels of the image type exceeds a predeterminedthreshold, and wherein exceeding the threshold is used to eliminate oneof the image types from possible consideration as a correct image typefor the object.
 52. The logic of claim 48, wherein the code is furtheroperable to: configure a policy that outlines one or more image typesthat are prohibited from being transmitted over the network.